def main():
arguments = specify_args()
arguments.debug = True
statistics_manager = StatisticsManager()
# Fixed Hyperparameters
# if use_actors = False, no other actors will be generated
use_actors = False
using_customized_route_and_scenario = True
multi_actors_scenarios = ['Scenario12']
arguments.scenarios = 'leaderboard/data/fuzzing_scenarios.json'
town_name = 'Town03'
scenario = 'Scenario12'
direction = 'front'
route = 0
# sample_factor is an integer between [1, 5]
sample_factor = 5
# waypoints_num_limit: the maximum number of waypoints that we consider to perturb.
waypoints_num_limit = 10
# lane_width = 3.5
max_num_of_vehicle = 2
max_num_of_pedestrians = 2
# Parameters to optimize
# Set up environment parameters
# real, [0, 1]
friction = 0.1
# integer, [0, 20]
weather_index = 2
# Laundry Stuff-------------------------------------------------------------
arguments.weather_index = weather_index
os.environ['WEATHER_INDEX'] = str(weather_index)
town_scenario_direction = town_name + '/' + scenario
folders = [os.environ['SAVE_FOLDER'], town_name, scenario]
if scenario in multi_actors_scenarios:
town_scenario_direction += '/' + direction
folders.append(direction)
cur_folder_name = make_hierarchical_dir(folders)
os.environ['SAVE_FOLDER'] = cur_folder_name
arguments.save_folder = os.environ['SAVE_FOLDER']
route_prefix = 'leaderboard/data/customized_routes/' + town_scenario_direction + '/route_'
route_str = str(route)
if route < 10:
route_str = '0' + route_str
arguments.routes = route_prefix + route_str + '.xml'
os.environ['ROUTES'] = arguments.routes
# extract waypoints along route
import xml.etree.ElementTree as ET
tree = ET.parse(arguments.routes)
route_waypoints = []
# this iteration should only go once since we only keep one route per file
for route in tree.iter("route"):
route_id = route.attrib['id']
route_town = route.attrib['town']
for waypoint in route.iter('waypoint'):
route_waypoints.append(
carla.Transform(
carla.Location(x=float(waypoint.attrib['x']),
y=float(waypoint.attrib['y']),
z=float(waypoint.attrib['z'])),
carla.Rotation(float(waypoint.attrib['pitch']),
float(waypoint.attrib['yaw']),
float(waypoint.attrib['roll']))))
# extract waypoints for the scenario
world_annotations = RouteParser.parse_annotations_file(arguments.scenarios)
info = world_annotations[town_name][0]["available_event_configurations"][0]
center = info["center"]
RouteParser.convert_waypoint_float(center)
center_location = carla.Location(float(center['x']), float(center['y']),
float(center['z']))
center_rotation = carla.Rotation(float(center['pitch']),
float(center['yaw']), 0.0)
center_transform = carla.Transform(center_location, center_rotation)
# --------------------------------------------------------------------------
if use_actors:
# Set up actors
# ego car
ego_car_waypoints_perturbation = []
for i in range(waypoints_num_limit):
dx = np.clip(np.random.normal(0, 2, 1)[0], -0.5, 0.5)
dy = np.clip(np.random.normal(0, 2, 1)[0], -0.5, 0.5)
ego_car_waypoints_perturbation.append((dx, dy))
# static
static_1_transform = center_transform
static_1 = Static(model='static.prop.barrel',
spawn_transform=static_1_transform)
static_list = [static_1]
# pedestrians
pedestrian_1_transform = create_transform(
route_waypoints[0].location.x - 2,
route_waypoints[0].location.y - 8, 0, 0,
route_waypoints[0].rotation.yaw, 0)
pedestrian_1 = Pedestrian(model='walker.pedestrian.0001',
spawn_transform=pedestrian_1_transform,
trigger_distance=20,
speed=1.5,
dist_to_travel=6,
after_trigger_behavior='stop')
pedestrian_list = [pedestrian_1]
# vehicles
waypoint_follower = True
vehicle_1_transform = create_transform(
route_waypoints[1].location.x, route_waypoints[1].location.y - 5,
0, 0, route_waypoints[1].rotation.yaw, 0)
# if waypoint_follower == False
vehicle_1_dist_to_travel = 5
vehicle_1_target_direction = carla.Vector3D(x=0.2, y=1, z=0)
# else
targeted_waypoint = create_transform(
route_waypoints[1].location.x, route_waypoints[1].location.y - 40,
0, 0, route_waypoints[1].rotation.yaw, 0)
# targeted_waypoint = route_waypoints[-1]
vehicle_1_waypoints_perturbation = []
for i in range(waypoints_num_limit):
dx = np.clip(np.random.normal(0, 2, 1)[0], -0.5, 0.5)
dy = np.clip(np.random.normal(0, 2, 1)[0], -0.5, 0.5)
vehicle_1_waypoints_perturbation.append((dx, dy))
vehicle_1 = Vehicle(
model='vehicle.audi.a2',
spawn_transform=vehicle_1_transform,
avoid_collision=True,
initial_speed=0,
trigger_distance=10,
waypoint_follower=waypoint_follower,
targeted_waypoint=targeted_waypoint,
dist_to_travel=vehicle_1_dist_to_travel,
target_direction=vehicle_1_target_direction,
targeted_speed=10,
after_trigger_behavior='stop',
color='(0, 0, 0)',
waypoints_perturbation=vehicle_1_waypoints_perturbation)
vehicle_list = [vehicle_1]
else:
static_list = []
pedestrian_list = []
vehicle_list = []
ego_car_waypoints_perturbation = []
customized_data = {
'friction': friction,
'static_list': static_list,
'pedestrian_list': pedestrian_list,
'vehicle_list': vehicle_list,
'center_transform': center_transform,
'using_customized_route_and_scenario': True,
'destination': route_waypoints[-1].location,
'sample_factor': sample_factor,
'ego_car_waypoints_perturbation': ego_car_waypoints_perturbation
}
try:
leaderboard_evaluator = LeaderboardEvaluator(arguments,
statistics_manager)
leaderboard_evaluator.run(arguments, customized_data)
except Exception as e:
traceback.print_exc()
finally:
del leaderboard_evaluator